@article {57957,
	title = {Spatiotemporal Patterns of the California Spotted Owl{\textquoteright}s Territorial Vocalizations},
	year = {2019},
	abstract = {<p>Emerging bioacoustic technology allows researchers to passively record animal vocalizations to study population dynamics at relatively broad spatial scales. Spatiotemporal patterns in vocalization behavior free from direct human influence, which occurs during traditional vocal-lure surveys, may also yield novel behavioral insights. We used passively recorded audio data to examine such patterns in the California Spotted Owl (Strix occidentalis occidentalis) across the northern Sierra Nevada. We assessed temporal patterns in the initiation of bouts of four-note calls and the number of such calls, and we used generalized linear mixed models to test whether environmental factors influenced bout-initiation time, bout duration, and the number of calls per unit time. We found that (1) Spotted Owls were most vocally active within three hours of sunset, (2) bouts of calling were longer with fewer calls per unit time in open and young forest, (3) those bouts were concentrated in the middle of the night, and (4) the frequency of occurrence and duration of bouts of territorial vocal activity was reduced in montane riparian forest. These patterns suggest that Spotted Owls may engage in territorial defense in marginal habitat (open and young forest) and minimize their vocal activity in key foraging habitat (montane riparian forest). This application of acoustic monitoring data to behavioral ecology illustrates the broad applicability of the underlying audio data and its potential to yield novel ecological insights.</p>
},
	doi = {10.21199/WB10.21199/WB50.410.21199/WB50.4.2},
	url = {https://www.westernfieldornithologists.org/publications.phphttps://www.westernfieldornithologists.org/archive/V50/journal-50-4.phphttps://www.westernfieldornithologists.org/archive/V50/50(4)-p232-p242.pdf},
	author = {Wood, Connor M. and Schmidt, Stephanie M. and Peery, M. Zachariah}
}
@article {57374,
	title = {Automated identification of avian vocalizations with deep convolutional neural networks},
	year = {2019},
	abstract = {<p>Passive acoustic monitoring is an emerging approach to wildlife monitoring that leverages recent improvements in automated recording units and other technolo- gies. A central challenge of this approach is the task of locating and identifying target species vocalizations in large volumes of audio data. To address this issue, we developed an efficient data processing pipeline using a deep convolutional neural network (CNN) to automate the detection of owl vocalizations in spec- trograms generated from unprocessed field recordings. While the project was ini- tially focused on spotted and barred owls, we also trained the network to recognize northern saw-whet owl, great horned owl, northern pygmy-owl, and western screech-owl. Although classification performance varies across species, initial results are promising. Recall, or the proportion of calls in the dataset that are detected and correctly identified, ranged from 63.1\% for barred owl to 91.5\% for spotted owl based on raw network output. Precision, the rate of true positives among apparent detections, ranged from 0.4\% for spotted owl to 77.1\% for northern saw-whet owl based on raw output. In limited tests, the CNN performed as well as or better than human technicians at detecting owl calls. Our model output is suitable for developing species encounter histories for occupancy models and other analyses. We believe our approach is sufficiently general to support long-term, large-scale monitoring of a broad range of species beyond our target species list, including birds, mammals, and others.</p>
},
	doi = {10.1002/rse2.125},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.125},
	author = {Ruff, Zachary J. and Lesmeister, Damon B. and Duchac, Leila S. and Padmaraju, Bharath K. and Sullivan, Christopher M.},
	editor = {Pettorelli, Nathalie and Lecours, Vincent}
}
